Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down at the end of May, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

ST: ST: st: Logistic regression & standardized coefficients; Multinomial GOF test


From   Ronald McDowell <McDowell-R3@email.ulster.ac.uk>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   ST: ST: st: Logistic regression & standardized coefficients; Multinomial GOF test
Date   Mon, 12 Dec 2011 08:54:41 +0000

Cam

Many thanks for the excellent references below.

Ron

Date: Thu, 8 Dec 2011 12:03:20 -0500
From: Cameron McIntosh <cnm100@hotmail.com>
Subject: RE: ST: st:  Logistic regression & standardized coefficients; Multinomial GOF test

Hi Ron,

It may make sense to standardize only with respect to X (predictors), which is easy to do. But maybe have a look at some of the more complicated formulas in:

Menard, S. (2011). Standards for Standardized Logistic Regression Coefficients. Social Forces, 89(4), 1409-1428.

Menard, S. (2004). Six Approaches to Calculating Standardized Logistic Regression Coefficients. The American Statistician, 58, 218-223.

I think it might be fairly straightforward to extend them to the nominal case. That said, you would have to think hard about whether it makes sense to standardize the predictors. This may only make sense with continuous predictors, and in my view it does not render effects comparable, because standard deviations differ across predictors and also across groups on the same predictor. I think the cautions in the following papers would apply:

King, G. (1986). How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science. American Journal of Political 
Science, 30(3), 666-687.http://gking.harvard.edu/files/mist.pdf>http://gking.harvard.edu/files/mist.pdf

Richards, J.M., Jr. (1982). Standardized versus Unstandardized Regression Weights. Applied Psychological Measurement, 6(2), 201-212. 
Greenland, S., Schlessman, J.J., & Criqui, M.H. (1986). The fallacy of employing standardized regression coefficients and correlations as measures of effect. American Journal of Epidemiology, 123, 203–208.
Greenland, S., Maclure, M., Schlessman, J.J., Poole, C., & Morgenstern, H. (1991). Standardized Regression Coefficients: A Further Critique and Review of Some Alternatives. Epidemiology, 2(5). 387-392. 
Criqui, M.H. (1991). On the Use of Standardized Regression Coefficients. Epidemiology, 2(5), 393.  
Hargens, L.L. (1976). A Note On Standardized Coefficients as Structural Parameters. Sociological Methods & Research, 5(2), 247-256

Kim, J.O., & Feree, G. D. (1981). Standardization in causal analysis. Sociological Methods and. Research, 10(2), 187–210.

Best,
Cam 
-----------------------------------------
Ron McDowell
Institute of Nursing Research
University of Ulster, Coleraine
McDowell-R3@email.ulster.ac.uk


*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index